The image texture can be regarded as a set of metrics quantifying the arrangement of pixel intensities in a digital image or a selected region of an image. Texture detection is an important issue in image processing. For example, digital images, still pictures as well as videos, exhibit some noise that has to be reduced by appropriately designed algorithms. The noise filtering, however, has to be adjusted according to the detected texture in order to avoid blurring of the image. If a selected region of an image is homogenous (flat), it can be heavily filtered since pixel variations are basically caused by noise. If, on the other hand, the selected region of an image is highly textured, pixel variations are mainly caused by the texture, and filtering should be performed lightly and with caution. Basically, only small differences from pixel-to-pixel shall be noise filtered.
In the art, texture detection is performed in the luminance domain and is based on high pass/low pass filtering. Conventional methods of texture detection are known as Sobel, Prewitt, or Canny maps. For example, the texture can be obtained from the Spatial Noise Level and the maximum of the absolute differences between a particular current pixel and its neighbors (see, for example, A. Bosco and M. Mancuso, “Adaptive Filtering for Image Denoising”, in IEEE Proceedings of ICCE2001, pages 208-209, 2001, which is incorporated by reference). However, despite the recent engineering progress, texture detection and noise reduction based on the same still are not sufficiently reliable and satisfying, in particular, in noisy images. The conventional methods not only are sensitive to noise, but also need appropriate fine tuning of thresholds in order to avoid a misclassification of pixels.